Adapting Protein Language Models for Explainable Fine-Grained Evolutionary Pattern Discovery

Organisms that live in different environments face different evolutionary pressures. As such, organisms that have more successful phenotypes reproduce more frequently, but differing selective pressures acting at the organismal level can influence genes, and thus proteins. Understanding how proteins adapt across environments may therefore be useful in engineering proteins for specific environments as well as to improve our understanding of basic biology. In this work, we explicitly compare homologous (read: paired) proteins from different environments. While previous studies have explored the relevant evolutionary pressures in one of these environments [11], [17] and genomic responses to those pressures [1], [28], no prior computational study of their proteins has been performed. We apply ESM-2 [20] and although there is no signal in our negative control (two divergent yeast strains) as expected, we obtain near perfect prediction accuracy for our selected environmental gradient–the well-established subsurface vs. surface biome. We further show that ESM-2 is able to capture relevant fine-grained biological patterns in its embedding space, even in its smallest model. Significantly, we demonstrate that these embeddings can be interpreted using a novel visualization pipeline built using explainable AI techniques.